
// g++ -DNDEBUG -O3 -I.. benchEigenSolver.cpp  -o benchEigenSolver && ./benchEigenSolver
// options:
//  -DBENCH_GMM
//  -DBENCH_GSL -lgsl /usr/lib/libcblas.so.3
//  -DEIGEN_DONT_VECTORIZE
//  -msse2
//  -DREPEAT=100
//  -DTRIES=10
//  -DSCALAR=double

#include <iostream>

#include <Eigen/Core>
#include <Eigen/QR>
#include <bench/BenchUtil.h>
using namespace Eigen;

#ifndef REPEAT
#    define REPEAT 1000
#endif

#ifndef TRIES
#    define TRIES 4
#endif

#ifndef SCALAR
#    define SCALAR float
#endif

typedef SCALAR Scalar;

template<typename MatrixType>
__attribute__((noinline)) void benchEigenSolver(const MatrixType& m)
{
    int rows = m.rows();
    int cols = m.cols();

    int stdRepeats = std::max(1, int((REPEAT * 1000) / (rows * rows * sqrt(rows))));
    int saRepeats  = stdRepeats * 4;

    typedef typename MatrixType::Scalar                                                  Scalar;
    typedef Matrix<Scalar, MatrixType::RowsAtCompileTime, MatrixType::RowsAtCompileTime> SquareMatrixType;

    MatrixType       a      = MatrixType::Random(rows, cols);
    SquareMatrixType covMat = a * a.adjoint();

    BenchTimer timerSa, timerStd;

    Scalar acc = 0;
    int    r   = internal::random<int>(0, covMat.rows() - 1);
    int    c   = internal::random<int>(0, covMat.cols() - 1);
    {
        SelfAdjointEigenSolver<SquareMatrixType> ei(covMat);
        for ( int t = 0; t < TRIES; ++t ) {
            timerSa.start();
            for ( int k = 0; k < saRepeats; ++k ) {
                ei.compute(covMat);
                acc += ei.eigenvectors().coeff(r, c);
            }
            timerSa.stop();
        }
    }

    {
        EigenSolver<SquareMatrixType> ei(covMat);
        for ( int t = 0; t < TRIES; ++t ) {
            timerStd.start();
            for ( int k = 0; k < stdRepeats; ++k ) {
                ei.compute(covMat);
                acc += ei.eigenvectors().coeff(r, c);
            }
            timerStd.stop();
        }
    }

    if ( MatrixType::RowsAtCompileTime == Dynamic )
        std::cout << "dyn   ";
    else
        std::cout << "fixed ";
    std::cout << covMat.rows() << " \t"
              << timerSa.value() * REPEAT / saRepeats << "s \t"
              << timerStd.value() * REPEAT / stdRepeats << "s";

#ifdef BENCH_GMM
    if ( MatrixType::RowsAtCompileTime == Dynamic ) {
        timerSa.reset();
        timerStd.reset();

        gmm::dense_matrix<Scalar> gmmCovMat(covMat.rows(), covMat.cols());
        gmm::dense_matrix<Scalar> eigvect(covMat.rows(), covMat.cols());
        std::vector<Scalar>       eigval(covMat.rows());
        eiToGmm(covMat, gmmCovMat);
        for ( int t = 0; t < TRIES; ++t ) {
            timerSa.start();
            for ( int k = 0; k < saRepeats; ++k ) {
                gmm::symmetric_qr_algorithm(gmmCovMat, eigval, eigvect);
                acc += eigvect(r, c);
            }
            timerSa.stop();
        }
        // the non-selfadjoint solver does not compute the eigen3 vectors
        //     for (int t=0; t<TRIES; ++t)
        //     {
        //       timerStd.start();
        //       for (int k=0; k<stdRepeats; ++k)
        //       {
        //         gmm::implicit_qr_algorithm(gmmCovMat, eigval, eigvect);
        //         acc += eigvect(r,c);
        //       }
        //       timerStd.stop();
        //     }

        std::cout << " | \t"
                  << timerSa.value() * REPEAT / saRepeats << "s"
                  << /*timerStd.value() * REPEAT / stdRepeats << "s"*/ "   na   ";
    }
#endif

#ifdef BENCH_GSL
    if ( MatrixType::RowsAtCompileTime == Dynamic ) {
        timerSa.reset();
        timerStd.reset();

        gsl_matrix*                gslCovMat = gsl_matrix_alloc(covMat.rows(), covMat.cols());
        gsl_matrix*                gslCopy   = gsl_matrix_alloc(covMat.rows(), covMat.cols());
        gsl_matrix*                eigvect   = gsl_matrix_alloc(covMat.rows(), covMat.cols());
        gsl_vector*                eigval    = gsl_vector_alloc(covMat.rows());
        gsl_eigen_symmv_workspace* eisymm    = gsl_eigen_symmv_alloc(covMat.rows());

        gsl_matrix_complex*           eigvectz  = gsl_matrix_complex_alloc(covMat.rows(), covMat.cols());
        gsl_vector_complex*           eigvalz   = gsl_vector_complex_alloc(covMat.rows());
        gsl_eigen_nonsymmv_workspace* einonsymm = gsl_eigen_nonsymmv_alloc(covMat.rows());

        eiToGsl(covMat, &gslCovMat);
        for ( int t = 0; t < TRIES; ++t ) {
            timerSa.start();
            for ( int k = 0; k < saRepeats; ++k ) {
                gsl_matrix_memcpy(gslCopy, gslCovMat);
                gsl_eigen_symmv(gslCopy, eigval, eigvect, eisymm);
                acc += gsl_matrix_get(eigvect, r, c);
            }
            timerSa.stop();
        }
        for ( int t = 0; t < TRIES; ++t ) {
            timerStd.start();
            for ( int k = 0; k < stdRepeats; ++k ) {
                gsl_matrix_memcpy(gslCopy, gslCovMat);
                gsl_eigen_nonsymmv(gslCopy, eigvalz, eigvectz, einonsymm);
                acc += GSL_REAL(gsl_matrix_complex_get(eigvectz, r, c));
            }
            timerStd.stop();
        }

        std::cout << " | \t"
                  << timerSa.value() * REPEAT / saRepeats << "s \t"
                  << timerStd.value() * REPEAT / stdRepeats << "s";

        gsl_matrix_free(gslCovMat);
        gsl_vector_free(gslCopy);
        gsl_matrix_free(eigvect);
        gsl_vector_free(eigval);
        gsl_matrix_complex_free(eigvectz);
        gsl_vector_complex_free(eigvalz);
        gsl_eigen_symmv_free(eisymm);
        gsl_eigen_nonsymmv_free(einonsymm);
    }
#endif

    std::cout << "\n";

    // make sure the compiler does not optimize too much
    if ( acc == 123 )
        std::cout << acc;
}

int main(int argc, char* argv[])
{
    const int dynsizes[] = {4, 6, 8, 12, 16, 24, 32, 64, 128, 256, 512, 0};
    std::cout << "size            selfadjoint       generic";
#ifdef BENCH_GMM
    std::cout << "        GMM++          ";
#endif
#ifdef BENCH_GSL
    std::cout << "       GSL (double + ATLAS)  ";
#endif
    std::cout << "\n";
    for ( uint i = 0; dynsizes[i] > 0; ++i )
        benchEigenSolver(Matrix<Scalar, Dynamic, Dynamic>(dynsizes[i], dynsizes[i]));

    benchEigenSolver(Matrix<Scalar, 2, 2>());
    benchEigenSolver(Matrix<Scalar, 3, 3>());
    benchEigenSolver(Matrix<Scalar, 4, 4>());
    benchEigenSolver(Matrix<Scalar, 6, 6>());
    benchEigenSolver(Matrix<Scalar, 8, 8>());
    benchEigenSolver(Matrix<Scalar, 12, 12>());
    benchEigenSolver(Matrix<Scalar, 16, 16>());
    return 0;
}
